import torch
from transformers import AutoTokenizer, AutoConfig, AutoModel, CLIPImageProcessor
import warnings
from PIL import Image
from .base import BaseModel
from ..smp import *
from ..dataset import DATASET_TYPE, DATASET_MODALITY
import pandas as pd
import string
import torch.distributed as dist
import torchvision.transforms as T
import transformers

from torchvision.transforms.functional import InterpolationMode
import re


IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose(
        [
            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(mean=MEAN, std=STD),
        ]
    )
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(
    image, min_num=1, max_num=4, image_size=448, use_thumbnail=False
):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    )
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size
    )

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image(image_file, input_size=448, max_num=6, upscale=False):
    image = Image.open(image_file).convert("RGB")
    if upscale:
        image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR)
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(
        image, image_size=input_size, use_thumbnail=True, max_num=max_num
    )
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


class VinternChat(BaseModel):

    INSTALL_REQ = False
    INTERLEAVE = True

    def __init__(
        self, model_path="5CD-AI/Vintern-3B-beta", load_in_8bit=False, **kwargs
    ):
        assert model_path is not None
        assert version_cmp(transformers.__version__, "4.36.2", "ge")

        self.model_path = model_path
        self.tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, use_fast=False
        )

        # Regular expression to match the pattern 'Image' followed by a number, e.g. Image1
        self.pattern = r"Image(\d+)"
        # Replacement pattern to insert a hyphen between 'Image' and the number, e.g. Image-1
        self.replacement = r"Image-\1"

        # Convert InternVL2 response to dataset format
        # e.g. Image1 -> Image-1

        # Regular expression to match the pattern 'Image-' followed by a number
        self.reverse_pattern = r"Image-(\d+)"
        # Replacement pattern to remove the hyphen (Image-1 -> Image1)
        self.reverse_replacement = r"Image\1"

        device = torch.cuda.current_device()
        self.device = device
        self.model = AutoModel.from_pretrained(
            model_path,
            torch_dtype=torch.bfloat16,
            trust_remote_code=True,
            load_in_8bit=load_in_8bit,
        ).eval()
        if not load_in_8bit:
            self.model = self.model.to(device)

        self.image_size = self.model.config.vision_config.image_size
        kwargs_default = dict(
            do_sample=False, max_new_tokens=1024, top_p=None, num_beams=3
        )
        kwargs_default.update(kwargs)
        self.kwargs = kwargs_default

        warnings.warn(
            f"Following kwargs received: {self.kwargs}, will use as generation config. "
        )

    def use_custom_prompt(self, dataset):
        if dataset is None:
            return False
        if listinstr(["MMDU", "MME-RealWorld", "MME-RealWorld-CN"], dataset):
            # For Multi-Turn we don't have custom prompt
            return False
        if DATASET_MODALITY(dataset) == "VIDEO":
            # For Video benchmarks we don't have custom prompt at here
            return False
        else:
            return True

    def build_multi_choice_prompt(self, line, dataset=None):
        question = line["question"]
        hint = line["hint"] if ("hint" in line and not pd.isna(line["hint"])) else None
        if hint is not None:
            question = hint + "\n" + question

        options = {
            cand: line[cand]
            for cand in string.ascii_uppercase
            if cand in line and not pd.isna(line[cand])
        }
        for key, item in options.items():
            question += f"\n{key}. {item}"
        prompt = question

        if len(options):
            prompt += (
                "\n请直接回答选项字母。"
                if cn_string(prompt)
                else "\nAnswer with the option's letter from the given choices directly."
            )
        else:
            prompt += (
                "\n请直接回答问题。"
                if cn_string(prompt)
                else "\nAnswer the question directly."
            )

        return prompt

    def build_video_prompt(self, prompt, dataset=None, max_frames=64):
        for start in range(0, max_frames, 8):
            images_to_remove = "".join(
                [f"<Image-{i}>" for i in range(start + 1, start + 9)]
            )
            prompt = prompt.replace(images_to_remove, "")
        for i in range(max_frames):
            prompt = prompt.replace(f"Image-{i + 1}", f"Frame-{i + 1}")
        if listinstr(["MMBench-Video"], dataset):
            prompt = prompt.replace("\nAnswer:", "")
        elif listinstr(["Video-MME"], dataset):
            prompt = prompt.replace("\nAnswer:", "")
            prompt += (
                "\nAnswer with the option's letter from the given choices directly."
            )
        elif listinstr(["MVBench"], dataset):
            prompt = prompt.replace("Best option:(", "")

        return prompt

    def build_prompt(self, line, dataset=None):
        assert self.use_custom_prompt(dataset)
        assert dataset is None or isinstance(dataset, str)
        tgt_path = self.dump_image(line, dataset)

        kwargs_default = dict(
            do_sample=False, max_new_tokens=1024, top_p=None, num_beams=3
        )

        if listinstr(["MTVQA"], dataset):
            kwargs_default["max_new_tokens"] = 256

        if listinstr(["MMMU_DEV_VAL", "MMMU_TEST"], dataset):
            kwargs_default["num_beams"] = 1

        self.kwargs = kwargs_default

        if dataset is not None and DATASET_TYPE(dataset) == "Y/N":
            question = line["question"]
            if listinstr(["MME"], dataset):
                prompt = (
                    question + " Answer the question using a single word or phrase."
                )
            elif listinstr(["HallusionBench"], dataset):
                prompt = (
                    question
                    + " Please answer yes or no. Answer the question using a single word or phrase."
                )
            else:
                prompt = line["question"]
        elif dataset is not None and DATASET_TYPE(dataset) == "MCQ":
            prompt = self.build_multi_choice_prompt(line, dataset)
        elif dataset is not None and DATASET_TYPE(dataset) == "VQA":
            question = line["question"]
            if listinstr(
                ["MathVista", "MathVision", "VCR", "MTVQA", "MMVet", "MathVerse"],
                dataset,
            ):
                prompt = question
            elif listinstr(["LLaVABench"], dataset):
                prompt = question + "\nAnswer this question in detail."
            else:
                prompt = (
                    question + "\nAnswer the question using a single word or phrase."
                )
        else:
            prompt = line["question"]
        message = [dict(type="text", value=prompt)]
        message.extend([dict(type="image", value=s) for s in tgt_path])
        return message

    def set_max_num(self, dataset):
        if dataset is None:
            self.max_num = 1
            return

        # res_1_datasets = ['MMBench-Video', 'Video-MME', 'MVBench', 'Video']
        res_12_datasets = [
            "ChartQA_TEST",
            "MMMU_DEV_VAL",
            "MMMU_TEST",
            "MME-RealWorld",
            "MME-RealWorld",
            "VCR_EN",
            "VCR_ZH",
        ]
        res_18_datasets = ["DocVQA_VAL", "DocVQA_TEST"]
        res_24_datasets = [
            "InfoVQA_VAL",
            "InfoVQA_TEST",
            "OCRBench",
            "HRBench4K",
            "HRBench8K",
        ]
        if DATASET_MODALITY(dataset) == "VIDEO":
            self.max_num = 1
        elif listinstr(res_12_datasets, dataset):
            self.max_num = 6  # 12
        elif listinstr(res_18_datasets, dataset):
            self.max_num = 6  # 18
        elif listinstr(res_24_datasets, dataset):
            self.max_num = 6  # 24
        elif listinstr(["MME"], dataset):
            self.max_num = 6  # 24
        else:
            self.max_num = 6  # 6

    def generate_v2(self, message, dataset=None):
        image_num = len([x for x in message if x["type"] == "image"])
        if image_num == 1:
            prompt = "<image>\n" + "\n".join(
                [x["value"] for x in message if x["type"] == "text"]
            )
        else:
            prompt, image_idx = "", 1
            for x in message:
                if x["type"] == "text":
                    prompt += x["value"]
                elif x["type"] == "image":
                    prompt += f"<Image-{image_idx}>"
                    image_idx += 1
            prompt = (
                "\n".join([f"Image-{i + 1}: <image>" for i in range(image_num)])
                + "\n"
                + prompt
            )

        if dataset is not None and DATASET_MODALITY(dataset) == "VIDEO":
            prompt = self.build_video_prompt(prompt, dataset)

        if image_num > 1:
            image_path = [x["value"] for x in message if x["type"] == "image"]
            num_patches_list = []
            pixel_values_list = []
            for image_idx, file_name in enumerate(image_path):
                upscale_flag = (
                    image_idx == 0
                    and dataset is not None
                    and listinstr(["MMMU_DEV_VAL"], dataset)
                )
                curr_pixel_values = (
                    load_image(file_name, max_num=self.max_num, upscale=upscale_flag)
                    .to(self.device)
                    .to(torch.bfloat16)
                )
                num_patches_list.append(curr_pixel_values.size(0))
                pixel_values_list.append(curr_pixel_values)
            pixel_values = torch.cat(pixel_values_list, dim=0)
        elif image_num == 1:
            image_path = [x["value"] for x in message if x["type"] == "image"][0]
            upscale_flag = dataset is not None and listinstr(["MMMU_DEV_VAL"], dataset)
            pixel_values = (
                load_image(image_path, max_num=self.max_num, upscale=upscale_flag)
                .to(self.device)
                .to(torch.bfloat16)
            )
            num_patches_list = [pixel_values.size(0)]
        else:
            pixel_values = None
            num_patches_list = []

        with torch.no_grad():
            response = self.model.chat(
                self.tokenizer,
                pixel_values=pixel_values,
                num_patches_list=num_patches_list,
                question=prompt,
                generation_config=self.kwargs,
                verbose=False,
            )
        return response

    def generate_inner(self, message, dataset=None):
        self.set_max_num(dataset)
        return self.generate_v2(message, dataset)

    def build_history(self, message):
        # Global Variables
        image_path = []
        image_cnt = 0

        def concat_tilist(tilist):
            nonlocal image_cnt  # Declare image_cnt as nonlocal to modify it
            prompt = ""
            for item in tilist:
                # Substitute the pattern in the text
                if item["type"] == "text":
                    prompt += re.sub(self.pattern, self.replacement, item["value"])
                elif item["type"] == "image":
                    image_cnt += 1
                    prompt += "<image>\n"
                    image_path.append(item["value"])
            return prompt

        # Only previous messages
        assert len(message) % 2 == 0
        history = []
        for i in range(len(message) // 2):
            m1, m2 = message[2 * i], message[2 * i + 1]
            assert m1["role"] == "user" and m2["role"] == "assistant"
            history.append((concat_tilist(m1["content"]), concat_tilist(m2["content"])))

        return history, image_path, image_cnt

    def chat_inner_v2(self, message, dataset=None):

        image_cnt = 0
        if len(message) > 1:
            history, image_path, image_cnt = self.build_history(message[:-1])
        else:
            history, image_path, image_cnt = None, [], 1
        current_msg = message[-1]
        question = ""

        # If message is just text in the conversation
        if (
            len(current_msg["content"]) == 1
            and current_msg["content"][0]["type"] == "text"
        ):
            question = current_msg["content"][0]["value"]
            question = re.sub(
                self.pattern, self.replacement, question
            )  # Fix pattern as per InternVL
        else:
            for msg in current_msg["content"]:
                if msg["type"] == "text":
                    question += re.sub(self.pattern, self.replacement, msg["value"])
                elif msg["type"] == "image":
                    image_cnt += 1
                    question += "<image>\n"
                    image_path.append(msg["value"])

        if image_cnt > 1:
            num_patches_list = []
            pixel_values_list = []
            for image_idx, file_name in enumerate(image_path):
                upscale_flag = (
                    image_idx == 0
                    and dataset is not None
                    and listinstr(["MMMU_DEV_VAL"], dataset)
                )
                curr_pixel_values = (
                    load_image(file_name, max_num=1, upscale=upscale_flag)
                    .to(self.device)
                    .to(torch.bfloat16)
                )
                num_patches_list.append(curr_pixel_values.size(0))
                pixel_values_list.append(curr_pixel_values)
            pixel_values = torch.cat(pixel_values_list, dim=0)
        elif image_cnt == 1:
            upscale_flag = dataset is not None and listinstr(["MMMU_DEV_VAL"], dataset)
            pixel_values = (
                load_image(image_path, max_num=self.max_num, upscale=upscale_flag)
                .to(self.device)
                .to(torch.bfloat16)
            )
            num_patches_list = [pixel_values.size(0)]
        else:
            pixel_values = None
            num_patches_list = []

        response, history = self.model.chat(
            self.tokenizer,
            pixel_values=pixel_values,
            num_patches_list=num_patches_list,
            question=question,
            generation_config=self.kwargs,
            history=history,
            return_history=True,
        )

        response = re.sub(self.reverse_pattern, self.reverse_replacement, response)

        return response

    def chat_inner(self, message, dataset=None):
        self.set_max_num(dataset)
        kwargs_default = dict(
            do_sample=False, max_new_tokens=512, top_p=None, num_beams=3
        )
        self.kwargs = kwargs_default
        return self.chat_inner_v2(message, dataset)
